Estimation of state of charge for hybrid unmanned aerial vehicle Li-ion power battery for considering rapid temperature change

Abstract

State of Charge (SOC) estimation of power battery is essential for hybrid Unmanned Aerial Vehicle (UAV) systems. During the flight of a hybrid UAV, the ambient temperature changes rapidly. The battery parameters also change with the temperature, which deteriorates the accuracy of SOC estimation. Given the above problems, this paper proposes a bipolar equivalent circuit model of a Li-ion battery and its R, C parameter identification method based on the particle swarm optimization algorithm, considering the impact of rapid changes in ambient temperature on battery parameters during the flight of a hybrid UAV. On this basis, a Fast Temperature Correction Adaptive Particle Filter (FTC-APF) SOC estimation method is proposed. This method not only considers the rapid change of temperature to update the actual capacity, Open Circuit Voltage (OCV), and Dual Polarization (DP) model parameters in real time but also weakens the impact of the uncertainty noise generated by external disturbances or sensors on the SOC estimation of Li-ion battery by predicting the process noise and updating the observation noise. The experimental results show that, compared with the existing research, the proposed method effectively achieves the accurate estimation of SOC of the hybrid UAV lithium-ion battery under the condition of rapid temperature change.